Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures

TL;DR

This paper introduces a deep learning-based method for real-time image classification and sorting of droplets containing single cells or 3D cultures, enabling high-throughput, accurate, and versatile cell analysis and isolation.

Contribution

The authors developed a rapid, accurate neural network approach for classifying droplet images in microfluidics, capable of identifying diverse cell types and enabling on-demand sorting.

Findings

Over 90% accuracy in cell identification

Real-time sorting at up to 40 droplets per second

Versatile classification across different object types

Abstract

Uncovering the heterogeneity of cell populations is a long-standing goal in fields ranging from antimicrobial resistance to cancer research. Emerging technology platforms such as droplet microfluidics hold the promise to decipher cellular heterogeneity at ultra-high-throughput. However, there is a lack of methods able to rapidly identify and isolate single cells or 3D cell cultures. Here we demonstrate that deep neural networks can accurately classify single droplet images in real-time based on the presence and number of micro-objects including single mammalian cells and multicellular spheroids. This approach also enables the identification of specific objects within mixtures of objects of different types and sizes. The training sets for the neural networks consisted of several hundred images manually picked and augmented to up to thousands of images per training class. Training required less than 10 minutes using a single GPU, and yielded accuracies of over 90% for single mammalian cell identification. Crucially, the same model could be used to classify different types of objects such as polystyrene spheres, polyacrylamide beads and MCF-7 cells. We applied the developed method to the selection of 3D cell cultures generated with Hek293FT cells encapsulated in agarose gel beads, highlighting the potential of the technology for the selection of objects with high diversity of visual appearance. The real-time sorting of single droplets was in-line with droplet generation and occurred at rates up to 40 per second independently of image size up to 480 x 480 pixels. The presented microfluidic device also enabled storage of sorted droplets to allow for downstream analyses.